On-demand webinar
Optimising battery tests: 4 hidden errors you can detect with AI
How using new anomaly detection tools can uncover errors you couldn’t find before.
Anomaly detection tools aren’t new. But the errors being uncovered by machine learning are.
When it comes to battery testing, not only do we have new data analysis techniques, but we also have new battery testing methods and equipment to work with.
This also means there are new errors to identify, and we need tools specifically designed to catch errors based on their application.
Battery testing
We have been in conversations with clients, partners, and others in the battery testing industry to discuss the important errors to catch and how we could build a tool that would do precisely that.
In this webinar we will share our findings, and talk through 4 common battery testing errors that can be uncovered using an anomaly detection solution, and how this impacts the overall lab efficiency.
Webinar highlights
- Next-generation anomaly detection: See how new anomaly detection techniques can be used to detect hidden errors in your test data.
- Overcoming common battery challenges: Learn more about the common pitfalls of battery testing and what labs around the world are doing to address them.
- Real-world Success stories: Hear about real cases where Monolith AI’s solutions have driven significant business and technical value.
- Leveraging data insights: Maximise the value of your data to improve decision-making and product development processes.
If you are unable to attend the live session, we would still encourage you to register to receive the webinar recording.
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Who should watch:
- Engineering lab managers who want to streamline their testing time by avoiding delays, and downstream costs of wasted experiments.
- Battery testing engineers who struggle with test equipment (faulty sensors, unreliable machinery, etc.) that causes experiments to fail without warning.
- Organisational leaders looking to improve overall lab throughput and implement cutting-edge machine learning data analysis techniques.
- Engineering teams struggling to leverage internal data science experts for scalable AI solutions.
